Indoor Drone Positioning Methods Using Wi-Fi RTT and Machine Learning

Abstract

For drones to expand their activities, a self-localization method for indoor flying drones is required to complement GPS. We have investigated indoor drone positioning based on Wi-Fi RTT (Round Trip Time). This paper presents methods for estimating the position coordinate of a drone using Wi-Fi RTT and machine learning. In addition to a method that learns actual Wi-Fi RTT ranging data, we propose a novel method that learns pseudo-generated ranging data reproducing Wi-Fi RTT characteristics. Experimental results show that the proposed machine learning-based method using pseudo-generated data achieves higher accuracy than the method that learns actual ranging data and is also superior to the conventional MMSE method.